Muscle electromyographic activity normalized to maximal muscle activity, not to M_max, better represents voluntary activation

Joanna Diong^1,2 Kenzo Kishimoto³ Jane E Butler^2,4 Martin E Héroux^2,4

¹School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, New South Wales, Australia
²Neuroscience Research Australia (NeuRA), Sydney, New South Wales, Australia
³School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, New South Wales, Australia
⁴School of Medical Sciences, University of New South Wales, New South Wales, Australia

Suggested citation

Diong J, Kishimoto K, Butler JE, Héroux ME (2022) Muscle electromyographic activity normalized to maximal muscle activity, not to M_max, better represents voluntary activation. PLOS One (in press).

Protocol registration

The protocol for this study was registered on the Open Science Framework (OSF): https://osf.io/7f3nk

This study is a secondary analysis of data from a related study, which is under the same registered protocol.
The description, data and code of the related study are available from the GitHub repository: https://github.com/joannadiong/Kishimoto_et_al_2021_JAP

Data

Raw data to generate Fig 1 are available in the zipped folder "sub22.zip" from the Open Science Foundation repository https://osf.io/wt7z8/ in these formats:

• Spike2 .smr
• Matlab .mat
• Text .txt

Unzip the subject data folder and place it in data -> raw. (Create the raw folder if it does not exist)

Processed data to generate Figs 2 and 3 are available from the data -> proc folder as the files:

• subjects_data.csv
• subjects_data.json
• subjects_times_mvc.csv

Code

Python code files (Python v3.8) were written by Joanna Diong with input from Martin Héroux.

Python files

script_proc: Main script to run analysis.

fig-1: Script to generate PNG and SVG files of single participant trial data (Fig 2), saved in data -> proc -> sub22

process, utilities, trials_key: Modules containing functions used to clean data and plot figures.

External dependency

process calls the deprecated Python package spike2py written by Martin Héroux. The deprecated package is bundled with this repo as "spike2py.zip" Download the package, save it in a location outside of this project folder, and point the Python interpreter (i.e. add the root) towards that location.

(For the new and revised packaged, see the spike2py GitHub page.)

Running Python code

A reliable way to reproduce the analysis would be to run the code in an integrated development environment for Python (e.g. PyCharm).

Create a virtual environment and install dependencies. Using the Terminal (Mac or Linux, or PyCharm Terminal),

python -m venv env

Next, activate the virtual environment.

For Mac or Linux,

source env/bin/activate

For Windows,

.\env\Scripts\activate

Then, install dependencies,

pip install -r requirements.txt

Download all code files and data.zip into a single folder. Unzip the data file into the same location. Download the spike2py package. Point the Python interpreter to the location of spike2py.

Run each file separately:

1. script_proc.py
2. fig-1.py

Output

Table 1. is generated using data from "results.txt", saved in data -> proc

Table 2. is generated using data from "subjects_data_describe.csv", saved in data -> proc

Fig 1. is generated using data from sub22, and saved in data -> proc -> sub22

Fig 2. is generated using data from "subjects_data.csv", saved as PNG and SVG files labelled "emg_torque" in data -> proc

Fig 3. is generated using data from "subjects_data.csv", saved as PNG and SVG files labelled "emg_normalised" in data -> proc